论文标题
限制玻尔兹曼机器流量和伊辛模型的临界温度
Restricted Boltzmann Machine Flows and The Critical Temperature of Ising models
论文作者
论文摘要
我们探索了由神经网络温度计，探索从限制的玻尔兹曼机器（RBM）中绘制在方形晶格ISING模型温度空间上的迭代采样（FLOW）的替代实验设置。引入了该框架，以探索基于RBM的深神经网络与重新归一化组（RG）之间的联系。已经发现，在某些条件下，在温度空间中训练有旋转配置接近的RBM的流量附近是一个关键值的值：$ k_b t_c / j \约2.269 $。在本文中，我们考虑了数据集，而没有有关模型拓扑的信息，即认为神经网络温度计不是检测RBM是否学习比例不变性的准确方法。
We explore alternative experimental setups for the iterative sampling (flow) from Restricted Boltzmann Machines (RBM) mapped on the temperature space of square lattice Ising models by a neural network thermometer. This framework has been introduced to explore connections between RBM-based deep neural networks and the Renormalization Group (RG). It has been found that, under certain conditions, the flow of an RBM trained with Ising spin configurations approaches in the temperature space a value around the critical one: $ k_B T_c / J \approx 2.269$. In this paper we consider datasets with no information about model topology to argue that a neural network thermometer is not an accurate way to detect whether the RBM has learned scale invariance or not.
